Super Resolution (SR) may refer to a method or algorithm for recovering a high-resolution image from one or several low-resolution versions of that image. Most SR methods operate under the assumption that the low-resolution input image was obtained by down-sampling the unknown high-resolution image with a known blur kernel. The blur kernel is typically assumed to be the Point Spread Function (PSF) of the camera; or, when the PSF value is unknown, the blur kernel is assumed to be a standard Low-Pass Filter (LPF) such as a Gaussian kernel or a bicubic kernel. Unfortunately, many SR algorithms fail to yield adequate results.
Photos often come out blurry due to camera shake, defocus or low-grade optics. Undoing this undesired effect is called de-blurring or deblurring, and most deblurring methods rely heavily on the availability of prior knowledge on the desired sharp image. Most existing algorithms rely, either explicitly or implicitly, on the fact that images contain enough step edges. This assumption is formulated in various ways. Some studies assume simple parametric probability models, which promote sparsity of image gradients. Other studies assume a parametric form for the spectrum of the image, which decays polynomially with frequency (corresponding to the Fourier transform of step edges). Many approaches employ heuristic methods for detecting and/or enhancing edges in the blurry image. These range from setting a threshold on the image gradients, to shock and bilateral filtering. Unfortunately, many de-blurring algorithms fail to yield adequate results.